It is the most basic training loss in deep da. Here, the assumption to be made is to have a small labeled target dataset to be used as a guide for transferring knowledge between different domains. After pre-training the network using the source data, the remaining layers of the target model are trained to exploit the target class label information. Ideally, the class label information is given directly in the supervised process, commonly using as the training loss \(L\)  the negative log-likelihood of the ground-truth class \(y_i\)  and the softmax predictions of the model \(\hat{y_i}\) , which represent class probabilities [1]}, [2]}, [3]}:
\(L = - \sum _{i=0}^{N} y_i \log \hat{y_i}.\) 
